A broadly deployed class of open-loop single-user multiple-input/multiple-output (MIMO) systems includes coded orthogonal frequency division multiplexing (OFDM) MIMO systems with bit-interleaved coded modulation (BICM). These MIMO systems have the potential to produce very large transmission rates, by providing very high spectral efficiencies (i.e., very large throughputs per unit bandwidth), and optimized diversity-transmission rate trade offs. However, the computational complexity of near-optimal receiver structures for these systems grows exponentially fast with increased spectral efficiencies. Designing receivers for coded OFDM/MIMO systems with BICM that have good performance-complexity trade-offs is thus a very important challenge in deploying reliable systems that can be operated with practical receivers and can achieve high-spectral efficiencies.
FIG. 1 illustrates a block diagram of a system for encoding of information-bearing signals for a single-user MIMO system. The signal is first encoded with an outer binary code, for example a convolutional code, which may be accomplished by convolutional coder 110. The coded signal is interleaved by interleaver 120 and mapped to vector parallel streams by serial-to-parallel converter 130. The parallel streams are modulated by mapper modems 140 and transmitted.
These coded OFDM/BICM/MIMO systems encode the information bearing signal with an outer binary code (e.g., convolutional code, turbo code, low-density parity check (LDPC) code) followed by a bit interleaver, a mapper of bits to symbols (e.g., quadrature amplitude modulation (QAM) symbols), followed by round robin transmission of these symbols over all transmit antennas via OFDM. The typical receivers for these systems have an inner-outer decoder structure, with soft-output decoder modules. The complexity of these receiver structures resides in the inner decoder, referred to as the inner joint demapper. Indeed, the complexity of these inner demappers grows exponentially fast with the number of bits transmitted per channel use, and as a result, exponentially fast with the system spectral efficiency (throughput per unit bandwidth).
A number of reduced complexity receiver structures have been proposed for these systems with various performance complexity trade-offs. One class of these techniques is based on inner demapper structures that make use of what is known as the soft-output M-Algorithm (SOMA) and its variants. The front-end operations that are performed prior to performing soft-SOMA inner decoding can have a significant impact in the performance of these schemes in iterative decoding structures. These designs perform jointly optimized front-end filtering and SOMA tree search and can be shown to significantly improve performance over other existing designs in iterative decoding.
For future wireless systems, it is desirable to have efficient utilization of the radio frequency spectrum in order to increase the data rate achievable within a given transmission bandwidth. This can be accomplished by employing multiple transmit and receive antennas combined with signal processing. A number of recently developed techniques and emerging standards are based on employing multiple antennas at a base station to improve the reliability of data communication over wireless media without compromising the effective data rate of the wireless systems. Specifically, recent advances in wireless communications have demonstrated that by jointly encoding symbols over time and transmit antennas at a base station one can obtain reliability (diversity) benefits as well as increases in the effective data rate from the base station to each cellular user.
These multiplexing (throughput) gains and diversity benefits depend on the space-time coding techniques employed at the base station. The multiplexing gains and diversity benefits are also inherently dependent on the number of transmit and receive antennas in the system being deployed. Specifically, these trade offs between multiplexing gains and diversity are fundamentally limited by the number of transmit and the number of receive antennas in the system.
For high data rates and wideband transmission the use of OFDM makes the equalizer unnecessary. With multilevel modems, coded modulation systems can be designed by means of an outer binary code, for example, a convolutional code and an interleaver in a bit-interleaved coded modulation (BICM) system.
Existing receiver structures for the transmission systems, for example, for coded MIMO/OFDM/BICM/ID systems, include an inner-outer decoder structure, whereby the outer decoder is optimally selected. The designs generally include the following features. Iterative decoding receivers using a MAP-based inner decoder and a MAP-based outer decoder yield the optimum bit-error-rate performance among all inner/outer decoder structures. However, the MAP-based inner decoder becomes computationally intractable as N (number of transmit antennas/number of QAM symbols that need to be jointly resolved) and B (number of bits represented by each QAM symbol) increase.
ID systems with a maxlogMAP-based inner decoder have less complexity than the MAP-based system and are asymptotically (high SNR) optimal in that they have near optimum bit-error-rate performance at high SNR. However, the maxlogMAP-based inner decoder also becomes computationally intractable as N and B increase.
Receivers using QRD/M-Algorithm based inner decoder also use a variant of the M-algorithm to produce hard bit estimates along with reliability information. As a result they can yield drastic reductions in complexity by proper choice of the M parameter, at a cost in bit-error-rate performance. These methods directly employ the “hard-output” M-algorithm, to generate hard-output estimates, and then employ the resulting M candidates to obtain soft information. However, to generate soft information for any bit location, both values of the bit must be available in the pool of the remaining M candidates. As a result, these methods resort to heuristic (and inferior) techniques to generate soft output for each bit. In addition these structures do not typically exploit iterative decoding.
MMSE-based inner decoders consisting of a linear MMSE-front end followed by QAM symbol/by QAM symbol soft-output generation methods have lower complexity, but suffer in bit-error-rate performance, especially at higher outer-code rates. At such rates higher-complexity inner decoders, which use QR-decomposition linear front-ends with channel-adaptive tree-search symbol-reordering, in order to perform tree searches that are based on a forward pass only or a forward-backward pass SOMA algorithm, have higher performance. However, these schemes are inferior to the proposed schemes in iterative decoding settings.